Overview

Dataset statistics

Number of variables28
Number of observations159120
Missing cells658430
Missing cells (%)14.8%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory23.7 MiB
Average record size in memory156.1 B

Variable types

Numeric22
Categorical6

Alerts

anio is highly overall correlated with estudiante and 1 other fieldsHigh correlation
SEDE is highly overall correlated with p_ext and 1 other fieldsHigh correlation
pa1 is highly overall correlated with pa2 and 1 other fieldsHigh correlation
pa2 is highly overall correlated with pa1 and 1 other fieldsHigh correlation
codCarrera is highly overall correlated with MATERIA and 1 other fieldsHigh correlation
rem2 is highly overall correlated with condiciĂ³nHigh correlation
estudiante is highly overall correlated with anio and 3 other fieldsHigh correlation
curso is highly overall correlated with anio and 2 other fieldsHigh correlation
p_ext is highly overall correlated with SEDE and 1 other fieldsHigh correlation
sala is highly overall correlated with SEDE and 3 other fieldsHigh correlation
pa1_prom is highly overall correlated with abandona2_pHigh correlation
edad is highly overall correlated with estudianteHigh correlation
prom_edad is highly overall correlated with cursoHigh correlation
abandona2_p is highly overall correlated with pa1_promHigh correlation
cuat is highly overall correlated with salaHigh correlation
MATERIA is highly overall correlated with codCarrera and 2 other fieldsHigh correlation
facultad is highly overall correlated with codCarrera and 1 other fieldsHigh correlation
extranjero is highly overall correlated with estudianteHigh correlation
condiciĂ³n is highly overall correlated with pa1 and 2 other fieldsHigh correlation
pa1 has 52355 (32.9%) missing valuesMissing
pa2 has 77813 (48.9%) missing valuesMissing
Final has 140696 (88.4%) missing valuesMissing
rem1 has 154013 (96.8%) missing valuesMissing
rem2 has 157512 (99.0%) missing valuesMissing
final_prom has 76041 (47.8%) missing valuesMissing
pa1 has 8558 (5.4%) zerosZeros
pa2 has 4007 (2.5%) zerosZeros
p_ext has 2096 (1.3%) zerosZeros
recurso has 111470 (70.1%) zerosZeros
p_recursa has 17184 (10.8%) zerosZeros
abandona2_p has 10873 (6.8%) zerosZeros

Reproduction

Analysis started2023-07-30 22:07:40.040560
Analysis finished2023-07-30 22:08:54.760427
Duration1 minute and 14.72 seconds
Software versionydata-profiling vv4.1.2
Download configurationconfig.json

Variables

anio
Real number (ℝ)

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2015.3839
Minimum2011
Maximum2019
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.4 MiB
2023-07-30T19:08:54.824268image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum2011
5-th percentile2011
Q12013
median2016
Q32018
95-th percentile2019
Maximum2019
Range8
Interquartile range (IQR)5

Descriptive statistics

Standard deviation2.6539449
Coefficient of variation (CV)0.0013168434
Kurtosis-1.3016371
Mean2015.3839
Median Absolute Deviation (MAD)2
Skewness-0.15127325
Sum3.2068789 Ă— 108
Variance7.0434237
MonotonicityIncreasing
2023-07-30T19:08:54.943290image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
2019 24705
15.5%
2018 22425
14.1%
2017 18766
11.8%
2013 17584
11.1%
2014 17336
10.9%
2012 15369
9.7%
2011 14498
9.1%
2015 14369
9.0%
2016 14068
8.8%
ValueCountFrequency (%)
2011 14498
9.1%
2012 15369
9.7%
2013 17584
11.1%
2014 17336
10.9%
2015 14369
9.0%
2016 14068
8.8%
2017 18766
11.8%
2018 22425
14.1%
2019 24705
15.5%
ValueCountFrequency (%)
2019 24705
15.5%
2018 22425
14.1%
2017 18766
11.8%
2016 14068
8.8%
2015 14369
9.0%
2014 17336
10.9%
2013 17584
11.1%
2012 15369
9.7%
2011 14498
9.1%

cuat
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
1
81022 
2
78098 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters159120
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 81022
50.9%
2 78098
49.1%

Length

2023-07-30T19:08:55.096271image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-30T19:08:55.213378image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
1 81022
50.9%
2 78098
49.1%

Most occurring characters

ValueCountFrequency (%)
1 81022
50.9%
2 78098
49.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 159120
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 81022
50.9%
2 78098
49.1%

Most occurring scripts

ValueCountFrequency (%)
Common 159120
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 81022
50.9%
2 78098
49.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 159120
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 81022
50.9%
2 78098
49.1%

SEDE
Real number (ℝ)

Distinct21
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.980455
Minimum1
Maximum42
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.4 MiB
2023-07-30T19:08:55.308878image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q36
95-th percentile21
Maximum42
Range41
Interquartile range (IQR)4

Descriptive statistics

Standard deviation7.4418268
Coefficient of variation (CV)1.244358
Kurtosis8.9863165
Mean5.980455
Median Absolute Deviation (MAD)2
Skewness2.8699064
Sum951610
Variance55.380786
MonotonicityNot monotonic
2023-07-30T19:08:55.409328image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
2 47571
29.9%
1 28631
18.0%
10 24496
15.4%
4 16030
 
10.1%
5 14729
 
9.3%
6 13230
 
8.3%
15 4723
 
3.0%
39 2486
 
1.6%
28 2383
 
1.5%
14 1236
 
0.8%
Other values (11) 3605
 
2.3%
ValueCountFrequency (%)
1 28631
18.0%
2 47571
29.9%
4 16030
 
10.1%
5 14729
 
9.3%
6 13230
 
8.3%
10 24496
15.4%
13 18
 
< 0.1%
14 1236
 
0.8%
15 4723
 
3.0%
21 774
 
0.5%
ValueCountFrequency (%)
42 680
 
0.4%
41 85
 
0.1%
39 2486
1.6%
35 93
 
0.1%
34 128
 
0.1%
33 435
 
0.3%
32 890
 
0.6%
31 287
 
0.2%
30 76
 
< 0.1%
28 2383
1.5%

MATERIA
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
53
109933 
3
49187 

Length

Max length2
Median length2
Mean length1.6908811
Min length1

Characters and Unicode

Total characters269053
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row3
3rd row3
4th row3
5th row3

Common Values

ValueCountFrequency (%)
53 109933
69.1%
3 49187
30.9%

Length

2023-07-30T19:08:55.517707image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-30T19:08:55.633302image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
53 109933
69.1%
3 49187
30.9%

Most occurring characters

ValueCountFrequency (%)
3 159120
59.1%
5 109933
40.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 269053
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 159120
59.1%
5 109933
40.9%

Most occurring scripts

ValueCountFrequency (%)
Common 269053
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 159120
59.1%
5 109933
40.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 269053
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 159120
59.1%
5 109933
40.9%

pa1
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct21
Distinct (%)< 0.1%
Missing52355
Missing (%)32.9%
Infinite0
Infinite (%)0.0%
Mean3.6465368
Minimum0
Maximum10
Zeros8558
Zeros (%)5.4%
Negative0
Negative (%)0.0%
Memory size2.4 MiB
2023-07-30T19:08:55.732588image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median3
Q35
95-th percentile8
Maximum10
Range10
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.4944638
Coefficient of variation (CV)0.68406379
Kurtosis-0.49223638
Mean3.6465368
Median Absolute Deviation (MAD)2
Skewness0.52378756
Sum389322.5
Variance6.2223496
MonotonicityNot monotonic
2023-07-30T19:08:55.844601image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
1 16019
 
10.1%
2 15799
 
9.9%
4 15782
 
9.9%
3 13702
 
8.6%
5 10070
 
6.3%
0 8558
 
5.4%
6 8203
 
5.2%
7 6373
 
4.0%
8 4617
 
2.9%
9 3000
 
1.9%
Other values (11) 4642
 
2.9%
(Missing) 52355
32.9%
ValueCountFrequency (%)
0 8558
5.4%
0.5 75
 
< 0.1%
1 16019
10.1%
1.5 338
 
0.2%
2 15799
9.9%
2.5 485
 
0.3%
3 13702
8.6%
3.5 465
 
0.3%
4 15782
9.9%
4.5 515
 
0.3%
ValueCountFrequency (%)
10 1491
 
0.9%
9.5 91
 
0.1%
9 3000
 
1.9%
8.5 177
 
0.1%
8 4617
2.9%
7.5 260
 
0.2%
7 6373
4.0%
6.5 328
 
0.2%
6 8203
5.2%
5.5 417
 
0.3%

pa2
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct22
Distinct (%)< 0.1%
Missing77813
Missing (%)48.9%
Infinite0
Infinite (%)0.0%
Mean4.2868056
Minimum0
Maximum10
Zeros4007
Zeros (%)2.5%
Negative0
Negative (%)0.0%
Memory size2.4 MiB
2023-07-30T19:08:55.974741image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median4
Q36
95-th percentile9
Maximum10
Range10
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.5293339
Coefficient of variation (CV)0.59002768
Kurtosis-0.70955882
Mean4.2868056
Median Absolute Deviation (MAD)2
Skewness0.28035647
Sum348547.3
Variance6.3975301
MonotonicityNot monotonic
2023-07-30T19:08:56.133524image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
4 13826
 
8.7%
2 10029
 
6.3%
3 9340
 
5.9%
5 9166
 
5.8%
1 8410
 
5.3%
6 7985
 
5.0%
7 6676
 
4.2%
8 5050
 
3.2%
0 4007
 
2.5%
9 3389
 
2.1%
Other values (12) 3429
 
2.2%
(Missing) 77813
48.9%
ValueCountFrequency (%)
0 4007
 
2.5%
0.5 38
 
< 0.1%
1 8410
5.3%
1.5 147
 
0.1%
2 10029
6.3%
2.5 254
 
0.2%
3 9340
5.9%
3.5 176
 
0.1%
4 13826
8.7%
4.5 303
 
0.2%
ValueCountFrequency (%)
10 1707
 
1.1%
9.5 39
 
< 0.1%
9 3389
2.1%
8.5 93
 
0.1%
8 5050
3.2%
7.8 1
 
< 0.1%
7.5 181
 
0.1%
7 6676
4.2%
6.5 237
 
0.1%
6 7985
5.0%

Final
Real number (ℝ)

Distinct11
Distinct (%)0.1%
Missing140696
Missing (%)88.4%
Infinite0
Infinite (%)0.0%
Mean4.0496092
Minimum0
Maximum10
Zeros81
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size2.4 MiB
2023-07-30T19:08:56.243871image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median4
Q36
95-th percentile8
Maximum10
Range10
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.1022376
Coefficient of variation (CV)0.51912109
Kurtosis-0.46235457
Mean4.0496092
Median Absolute Deviation (MAD)2
Skewness0.52150845
Sum74610
Variance4.4194028
MonotonicityNot monotonic
2023-07-30T19:08:56.342192image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
2 3791
 
2.4%
3 3161
 
2.0%
4 2964
 
1.9%
5 2379
 
1.5%
6 1897
 
1.2%
7 1467
 
0.9%
1 1419
 
0.9%
8 774
 
0.5%
9 378
 
0.2%
10 113
 
0.1%
(Missing) 140696
88.4%
ValueCountFrequency (%)
0 81
 
0.1%
1 1419
 
0.9%
2 3791
2.4%
3 3161
2.0%
4 2964
1.9%
5 2379
1.5%
6 1897
1.2%
7 1467
 
0.9%
8 774
 
0.5%
9 378
 
0.2%
ValueCountFrequency (%)
10 113
 
0.1%
9 378
 
0.2%
8 774
 
0.5%
7 1467
 
0.9%
6 1897
1.2%
5 2379
1.5%
4 2964
1.9%
3 3161
2.0%
2 3791
2.4%
1 1419
 
0.9%

codCarrera
Real number (ℝ)

Distinct92
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean120.68924
Minimum1
Maximum999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.4 MiB
2023-07-30T19:08:56.472835image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile9
Q130
median39
Q345
95-th percentile999
Maximum999
Range998
Interquartile range (IQR)15

Descriptive statistics

Standard deviation266.73499
Coefficient of variation (CV)2.2100975
Kurtosis6.8586517
Mean120.68924
Median Absolute Deviation (MAD)7
Skewness2.9590985
Sum19204072
Variance71147.557
MonotonicityNot monotonic
2023-07-30T19:08:56.616329image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
39 49507
31.1%
999 13328
 
8.4%
45 8602
 
5.4%
40 7143
 
4.5%
90 6745
 
4.2%
33 6116
 
3.8%
41 5846
 
3.7%
11 5123
 
3.2%
9 4937
 
3.1%
30 4513
 
2.8%
Other values (82) 47260
29.7%
ValueCountFrequency (%)
1 362
 
0.2%
2 41
 
< 0.1%
4 2299
1.4%
5 2456
1.5%
6 43
 
< 0.1%
7 292
 
0.2%
8 7
 
< 0.1%
9 4937
3.1%
10 5
 
< 0.1%
11 5123
3.2%
ValueCountFrequency (%)
999 13328
8.4%
140 64
 
< 0.1%
138 2
 
< 0.1%
137 11
 
< 0.1%
136 1
 
< 0.1%
135 200
 
0.1%
134 32
 
< 0.1%
133 464
 
0.3%
132 2794
 
1.8%
131 23
 
< 0.1%

facultad
Categorical

Distinct15
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
MEDICINA
70412 
INGENIERIA
26784 
CS EXACTAS Y NATURALES
15876 
999.0
13328 
CIENCIAS VETERINARIAS
8637 
Other values (10)
24083 

Length

Max length24
Median length22
Mean length11.031058
Min length4

Characters and Unicode

Total characters1755262
Distinct characters26
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowINGENIERIA
2nd rowCS EXACTAS Y NATURALES
3rd rowINGENIERIA
4th rowINGENIERIA
5th rowINGENIERIA

Common Values

ValueCountFrequency (%)
MEDICINA 70412
44.3%
INGENIERIA 26784
 
16.8%
CS EXACTAS Y NATURALES 15876
 
10.0%
999.0 13328
 
8.4%
CIENCIAS VETERINARIAS 8637
 
5.4%
FARMACIA Y BIOQUIMICA 7632
 
4.8%
ODONTOLOGIA 5846
 
3.7%
AGRONOMIA 3914
 
2.5%
99.0 3534
 
2.2%
ARQUITECTURA Y URBANISMO 755
 
0.5%
Other values (5) 2402
 
1.5%

Length

2023-07-30T19:08:56.751432image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
medicina 70412
30.1%
ingenieria 26784
 
11.4%
y 24656
 
10.5%
cs 15876
 
6.8%
exactas 15876
 
6.8%
naturales 15876
 
6.8%
999.0 13328
 
5.7%
ciencias 9688
 
4.1%
veterinarias 8637
 
3.7%
farmacia 7632
 
3.3%
Other values (12) 25231
 
10.8%

Most occurring characters

ValueCountFrequency (%)
I 302541
17.2%
A 236506
13.5%
E 185729
10.6%
N 169451
9.7%
C 140323
8.0%
M 91100
 
5.2%
D 76676
 
4.4%
74876
 
4.3%
R 74556
 
4.2%
S 69381
 
4.0%
Other values (16) 334123
19.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 1599610
91.1%
Space Separator 74876
 
4.3%
Decimal Number 63914
 
3.6%
Other Punctuation 16862
 
1.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
I 302541
18.9%
A 236506
14.8%
E 185729
11.6%
N 169451
10.6%
C 140323
8.8%
M 91100
 
5.7%
D 76676
 
4.8%
R 74556
 
4.7%
S 69381
 
4.3%
T 48138
 
3.0%
Other values (12) 205209
12.8%
Decimal Number
ValueCountFrequency (%)
9 47052
73.6%
0 16862
 
26.4%
Space Separator
ValueCountFrequency (%)
74876
100.0%
Other Punctuation
ValueCountFrequency (%)
. 16862
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1599610
91.1%
Common 155652
 
8.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
I 302541
18.9%
A 236506
14.8%
E 185729
11.6%
N 169451
10.6%
C 140323
8.8%
M 91100
 
5.7%
D 76676
 
4.8%
R 74556
 
4.7%
S 69381
 
4.3%
T 48138
 
3.0%
Other values (12) 205209
12.8%
Common
ValueCountFrequency (%)
74876
48.1%
9 47052
30.2%
. 16862
 
10.8%
0 16862
 
10.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1755262
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
I 302541
17.2%
A 236506
13.5%
E 185729
10.6%
N 169451
9.7%
C 140323
8.0%
M 91100
 
5.2%
D 76676
 
4.4%
74876
 
4.3%
R 74556
 
4.2%
S 69381
 
4.0%
Other values (16) 334123
19.0%

rem1
Real number (ℝ)

Distinct11
Distinct (%)0.2%
Missing154013
Missing (%)96.8%
Infinite0
Infinite (%)0.0%
Mean4.0569806
Minimum0
Maximum10
Zeros20
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size2.4 MiB
2023-07-30T19:08:56.873076image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median4
Q36
95-th percentile8
Maximum10
Range10
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.0898548
Coefficient of variation (CV)0.51512564
Kurtosis-0.45301489
Mean4.0569806
Median Absolute Deviation (MAD)2
Skewness0.51890656
Sum20719
Variance4.3674929
MonotonicityNot monotonic
2023-07-30T19:08:56.971395image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
2 1066
 
0.7%
4 854
 
0.5%
3 841
 
0.5%
5 668
 
0.4%
6 543
 
0.3%
7 380
 
0.2%
1 379
 
0.2%
8 222
 
0.1%
9 105
 
0.1%
10 29
 
< 0.1%
(Missing) 154013
96.8%
ValueCountFrequency (%)
0 20
 
< 0.1%
1 379
 
0.2%
2 1066
0.7%
3 841
0.5%
4 854
0.5%
5 668
0.4%
6 543
0.3%
7 380
 
0.2%
8 222
 
0.1%
9 105
 
0.1%
ValueCountFrequency (%)
10 29
 
< 0.1%
9 105
 
0.1%
8 222
 
0.1%
7 380
 
0.2%
6 543
0.3%
5 668
0.4%
4 854
0.5%
3 841
0.5%
2 1066
0.7%
1 379
 
0.2%

rem2
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct11
Distinct (%)0.7%
Missing157512
Missing (%)99.0%
Infinite0
Infinite (%)0.0%
Mean3.7723881
Minimum0
Maximum10
Zeros11
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size2.4 MiB
2023-07-30T19:08:57.074433image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median3
Q35
95-th percentile7
Maximum10
Range10
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.0425916
Coefficient of variation (CV)0.54145851
Kurtosis-0.44937404
Mean3.7723881
Median Absolute Deviation (MAD)1
Skewness0.5299905
Sum6066
Variance4.1721805
MonotonicityNot monotonic
2023-07-30T19:08:57.171349image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
2 381
 
0.2%
3 245
 
0.2%
4 236
 
0.1%
5 221
 
0.1%
1 170
 
0.1%
6 164
 
0.1%
7 105
 
0.1%
8 50
 
< 0.1%
9 19
 
< 0.1%
0 11
 
< 0.1%
(Missing) 157512
99.0%
ValueCountFrequency (%)
0 11
 
< 0.1%
1 170
0.1%
2 381
0.2%
3 245
0.2%
4 236
0.1%
5 221
0.1%
6 164
0.1%
7 105
 
0.1%
8 50
 
< 0.1%
9 19
 
< 0.1%
ValueCountFrequency (%)
10 6
 
< 0.1%
9 19
 
< 0.1%
8 50
 
< 0.1%
7 105
 
0.1%
6 164
0.1%
5 221
0.1%
4 236
0.1%
3 245
0.2%
2 381
0.2%
1 170
0.1%

estudiante
Real number (ℝ)

Distinct120364
Distinct (%)75.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean79518.568
Minimum0
Maximum165534
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size2.4 MiB
2023-07-30T19:08:57.297776image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile7947.9
Q137405.75
median74470
Q3122336
95-th percentile157065.05
Maximum165534
Range165534
Interquartile range (IQR)84930.25

Descriptive statistics

Standard deviation48612.542
Coefficient of variation (CV)0.61133574
Kurtosis-1.239625
Mean79518.568
Median Absolute Deviation (MAD)41762.5
Skewness0.13869823
Sum1.2652995 Ă— 1010
Variance2.3631792 Ă— 109
MonotonicityNot monotonic
2023-07-30T19:08:57.434968image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
31 14
 
< 0.1%
415 12
 
< 0.1%
28 11
 
< 0.1%
6233 11
 
< 0.1%
2262 10
 
< 0.1%
142813 10
 
< 0.1%
19820 10
 
< 0.1%
369 9
 
< 0.1%
45736 9
 
< 0.1%
145792 9
 
< 0.1%
Other values (120354) 159015
99.9%
ValueCountFrequency (%)
0 1
 
< 0.1%
1 1
 
< 0.1%
2 2
< 0.1%
4 1
 
< 0.1%
5 2
< 0.1%
7 1
 
< 0.1%
9 1
 
< 0.1%
10 3
< 0.1%
11 1
 
< 0.1%
12 3
< 0.1%
ValueCountFrequency (%)
165534 1
< 0.1%
165532 1
< 0.1%
165531 1
< 0.1%
165530 1
< 0.1%
165528 1
< 0.1%
165526 1
< 0.1%
165525 1
< 0.1%
165524 1
< 0.1%
165523 1
< 0.1%
165522 1
< 0.1%

extranjero
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
0
134473 
1
24647 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters159120
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 134473
84.5%
1 24647
 
15.5%

Length

2023-07-30T19:08:57.561637image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-30T19:08:57.676318image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 134473
84.5%
1 24647
 
15.5%

Most occurring characters

ValueCountFrequency (%)
0 134473
84.5%
1 24647
 
15.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 159120
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 134473
84.5%
1 24647
 
15.5%

Most occurring scripts

ValueCountFrequency (%)
Common 159120
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 134473
84.5%
1 24647
 
15.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 159120
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 134473
84.5%
1 24647
 
15.5%

curso
Real number (ℝ)

Distinct1763
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1415.3966
Minimum0
Maximum2663
Zeros71
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size2.4 MiB
2023-07-30T19:08:57.787317image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile130
Q1728
median1469
Q32158
95-th percentile2542
Maximum2663
Range2663
Interquartile range (IQR)1430

Descriptive statistics

Standard deviation796.08922
Coefficient of variation (CV)0.56244957
Kurtosis-1.2913619
Mean1415.3966
Median Absolute Deviation (MAD)711
Skewness-0.12349773
Sum2.2521791 Ă— 108
Variance633758.04
MonotonicityNot monotonic
2023-07-30T19:08:57.930000image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2594 205
 
0.1%
1691 202
 
0.1%
813 191
 
0.1%
2294 190
 
0.1%
2593 184
 
0.1%
1690 177
 
0.1%
814 175
 
0.1%
2539 174
 
0.1%
2469 173
 
0.1%
2528 172
 
0.1%
Other values (1753) 157277
98.8%
ValueCountFrequency (%)
0 71
< 0.1%
1 58
< 0.1%
2 78
< 0.1%
3 75
< 0.1%
4 42
< 0.1%
5 68
< 0.1%
6 59
< 0.1%
7 83
0.1%
8 59
< 0.1%
9 60
< 0.1%
ValueCountFrequency (%)
2663 99
0.1%
2662 87
0.1%
2660 85
0.1%
2647 64
< 0.1%
2646 109
0.1%
2645 102
0.1%
2644 111
0.1%
2643 1
 
< 0.1%
2642 19
 
< 0.1%
2641 34
 
< 0.1%

turno
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
C
53786 
B
46516 
A
42316 
D
16502 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters159120
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA
2nd rowA
3rd rowA
4th rowA
5th rowA

Common Values

ValueCountFrequency (%)
C 53786
33.8%
B 46516
29.2%
A 42316
26.6%
D 16502
 
10.4%

Length

2023-07-30T19:08:58.056309image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-30T19:08:58.814521image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
c 53786
33.8%
b 46516
29.2%
a 42316
26.6%
d 16502
 
10.4%

Most occurring characters

ValueCountFrequency (%)
C 53786
33.8%
B 46516
29.2%
A 42316
26.6%
D 16502
 
10.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 159120
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
C 53786
33.8%
B 46516
29.2%
A 42316
26.6%
D 16502
 
10.4%

Most occurring scripts

ValueCountFrequency (%)
Latin 159120
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
C 53786
33.8%
B 46516
29.2%
A 42316
26.6%
D 16502
 
10.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 159120
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
C 53786
33.8%
B 46516
29.2%
A 42316
26.6%
D 16502
 
10.4%

n_alum
Real number (ℝ)

Distinct174
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean103.0319
Minimum1
Maximum205
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.4 MiB
2023-07-30T19:08:58.949621image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile50
Q182
median102
Q3126
95-th percentile154
Maximum205
Range204
Interquartile range (IQR)44

Descriptive statistics

Standard deviation31.938529
Coefficient of variation (CV)0.30998679
Kurtosis-0.27445453
Mean103.0319
Median Absolute Deviation (MAD)22
Skewness0.038333231
Sum16394436
Variance1020.0696
MonotonicityNot monotonic
2023-07-30T19:08:59.089784image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
98 3038
 
1.9%
89 3026
 
1.9%
86 2580
 
1.6%
92 2392
 
1.5%
113 2373
 
1.5%
112 2352
 
1.5%
84 2352
 
1.5%
103 2163
 
1.4%
97 2134
 
1.3%
85 2125
 
1.3%
Other values (164) 134585
84.6%
ValueCountFrequency (%)
1 3
 
< 0.1%
2 2
 
< 0.1%
3 3
 
< 0.1%
5 5
 
< 0.1%
7 7
 
< 0.1%
8 16
 
< 0.1%
11 11
 
< 0.1%
12 12
 
< 0.1%
13 13
 
< 0.1%
14 56
< 0.1%
ValueCountFrequency (%)
205 205
0.1%
202 202
0.1%
191 191
0.1%
190 190
0.1%
184 184
0.1%
177 177
0.1%
175 175
0.1%
174 174
0.1%
173 173
0.1%
172 172
0.1%

p_ext
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct999
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.15489568
Minimum0
Maximum1
Zeros2096
Zeros (%)1.3%
Negative0
Negative (%)0.0%
Memory size2.4 MiB
2023-07-30T19:08:59.244669image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.022900763
Q10.071428571
median0.13513514
Q30.21100917
95-th percentile0.37349398
Maximum1
Range1
Interquartile range (IQR)0.1395806

Descriptive statistics

Standard deviation0.11016795
Coefficient of variation (CV)0.7112397
Kurtosis2.236586
Mean0.15489568
Median Absolute Deviation (MAD)0.067112056
Skewness1.267695
Sum24647
Variance0.012136978
MonotonicityNot monotonic
2023-07-30T19:08:59.388771image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2096
 
1.3%
0.1666666667 1386
 
0.9%
0.1111111111 1170
 
0.7%
0.07692307692 1027
 
0.6%
0.1428571429 987
 
0.6%
0.09090909091 913
 
0.6%
0.09523809524 903
 
0.6%
0.125 776
 
0.5%
0.0625 752
 
0.5%
0.25 672
 
0.4%
Other values (989) 148438
93.3%
ValueCountFrequency (%)
0 2096
1.3%
0.006993006993 143
 
0.1%
0.008474576271 118
 
0.1%
0.008620689655 116
 
0.1%
0.009900990099 101
 
0.1%
0.01 100
 
0.1%
0.0101010101 99
 
0.1%
0.01111111111 90
 
0.1%
0.01123595506 89
 
0.1%
0.01149425287 87
 
0.1%
ValueCountFrequency (%)
1 1
 
< 0.1%
0.7692307692 117
0.1%
0.7142857143 147
0.1%
0.619047619 147
0.1%
0.6181818182 110
0.1%
0.5555555556 81
0.1%
0.5542168675 83
0.1%
0.5384615385 143
0.1%
0.5346534653 101
0.1%
0.5338983051 118
0.1%

recurso
Real number (ℝ)

Distinct15
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.44417421
Minimum0
Maximum14
Zeros111470
Zeros (%)70.1%
Negative0
Negative (%)0.0%
Memory size2.4 MiB
2023-07-30T19:08:59.508377image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile2
Maximum14
Range14
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.8268404
Coefficient of variation (CV)1.8615228
Kurtosis11.449027
Mean0.44417421
Median Absolute Deviation (MAD)0
Skewness2.6519903
Sum70677
Variance0.68366505
MonotonicityNot monotonic
2023-07-30T19:08:59.603305image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
0 111470
70.1%
1 32033
 
20.1%
2 10669
 
6.7%
3 3436
 
2.2%
4 1006
 
0.6%
5 282
 
0.2%
6 120
 
0.1%
7 48
 
< 0.1%
8 28
 
< 0.1%
9 13
 
< 0.1%
Other values (5) 15
 
< 0.1%
ValueCountFrequency (%)
0 111470
70.1%
1 32033
 
20.1%
2 10669
 
6.7%
3 3436
 
2.2%
4 1006
 
0.6%
5 282
 
0.2%
6 120
 
0.1%
7 48
 
< 0.1%
8 28
 
< 0.1%
9 13
 
< 0.1%
ValueCountFrequency (%)
14 1
 
< 0.1%
13 1
 
< 0.1%
12 3
 
< 0.1%
11 4
 
< 0.1%
10 6
 
< 0.1%
9 13
 
< 0.1%
8 28
 
< 0.1%
7 48
 
< 0.1%
6 120
0.1%
5 282
0.2%

p_recursa
Real number (ℝ)

Distinct1162
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.29945953
Minimum0
Maximum0.87142857
Zeros17184
Zeros (%)10.8%
Negative0
Negative (%)0.0%
Memory size2.4 MiB
2023-07-30T19:08:59.741177image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.047619048
median0.31182796
Q30.49056604
95-th percentile0.66304348
Maximum0.87142857
Range0.87142857
Interquartile range (IQR)0.44294699

Descriptive statistics

Standard deviation0.22781388
Coefficient of variation (CV)0.76075014
Kurtosis-1.248711
Mean0.29945953
Median Absolute Deviation (MAD)0.22150538
Skewness0.11721455
Sum47650
Variance0.051899163
MonotonicityNot monotonic
2023-07-30T19:08:59.873622image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 17184
 
10.8%
0.3333333333 1614
 
1.0%
0.5 1296
 
0.8%
0.3636363636 649
 
0.4%
0.6363636364 473
 
0.3%
0.2 455
 
0.3%
0.6 445
 
0.3%
0.4642857143 420
 
0.3%
0.1818181818 418
 
0.3%
0.02941176471 408
 
0.3%
Other values (1152) 135758
85.3%
ValueCountFrequency (%)
0 17184
10.8%
0.006172839506 162
 
0.1%
0.006289308176 318
 
0.2%
0.006493506494 154
 
0.1%
0.007575757576 132
 
0.1%
0.007874015748 127
 
0.1%
0.007936507937 126
 
0.1%
0.008 125
 
0.1%
0.008130081301 123
 
0.1%
0.00826446281 121
 
0.1%
ValueCountFrequency (%)
0.8714285714 140
0.1%
0.8113207547 53
 
< 0.1%
0.7916666667 24
 
< 0.1%
0.7763157895 76
 
< 0.1%
0.7702702703 74
 
< 0.1%
0.7692307692 247
0.2%
0.7583892617 149
0.1%
0.7558139535 86
 
0.1%
0.75 280
0.2%
0.7432432432 74
 
< 0.1%

sala
Real number (ℝ)

Distinct150
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean48.283277
Minimum0
Maximum187
Zeros1574
Zeros (%)1.0%
Negative0
Negative (%)0.0%
Memory size2.4 MiB
2023-07-30T19:09:00.015925image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4
Q117
median32
Q376
95-th percentile120
Maximum187
Range187
Interquartile range (IQR)59

Descriptive statistics

Standard deviation39.627475
Coefficient of variation (CV)0.82072878
Kurtosis1.0169363
Mean48.283277
Median Absolute Deviation (MAD)25
Skewness1.0890987
Sum7682835
Variance1570.3367
MonotonicityNot monotonic
2023-07-30T19:09:00.155332image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
27 9764
 
6.1%
15 6958
 
4.4%
5 6407
 
4.0%
84 6282
 
3.9%
10 5682
 
3.6%
4 5430
 
3.4%
94 4975
 
3.1%
86 4075
 
2.6%
24 3942
 
2.5%
26 3632
 
2.3%
Other values (140) 101973
64.1%
ValueCountFrequency (%)
0 1574
 
1.0%
1 734
 
0.5%
2 554
 
0.3%
3 444
 
0.3%
4 5430
3.4%
5 6407
4.0%
6 534
 
0.3%
7 1353
 
0.9%
8 959
 
0.6%
9 1476
 
0.9%
ValueCountFrequency (%)
187 269
 
0.2%
186 411
 
0.3%
185 85
 
0.1%
182 1
 
< 0.1%
181 113
 
0.1%
180 451
 
0.3%
179 1191
0.7%
178 321
 
0.2%
177 409
 
0.3%
165 62
 
< 0.1%

pa1_prom
Real number (ℝ)

Distinct1487
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.6269842
Minimum1.3484848
Maximum7.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.4 MiB
2023-07-30T19:09:00.309223image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1.3484848
5-th percentile2.295082
Q13
median3.5862069
Q34.1724138
95-th percentile5.1594203
Maximum7.8
Range6.4515152
Interquartile range (IQR)1.1724138

Descriptive statistics

Standard deviation0.8614223
Coefficient of variation (CV)0.23750374
Kurtosis0.14754441
Mean3.6269842
Median Absolute Deviation (MAD)0.5862069
Skewness0.37377638
Sum577125.72
Variance0.74204838
MonotonicityNot monotonic
2023-07-30T19:09:00.444026image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4 1217
 
0.8%
3.5 901
 
0.6%
3 723
 
0.5%
2.5 530
 
0.3%
3.4 447
 
0.3%
4.285714286 398
 
0.3%
3.1 373
 
0.2%
3.25 371
 
0.2%
3.854545455 367
 
0.2%
5 355
 
0.2%
Other values (1477) 153438
96.4%
ValueCountFrequency (%)
1.348484848 120
0.1%
1.43902439 80
0.1%
1.509090909 141
0.1%
1.61038961 100
0.1%
1.625 102
0.1%
1.636363636 115
0.1%
1.666666667 14
 
< 0.1%
1.738095238 56
 
< 0.1%
1.803921569 103
0.1%
1.807692308 63
< 0.1%
ValueCountFrequency (%)
7.8 15
 
< 0.1%
7.333333333 3
 
< 0.1%
7.206896552 38
< 0.1%
7.1 17
 
< 0.1%
7 81
0.1%
6.625 20
 
< 0.1%
6.5 76
< 0.1%
6.444444444 19
 
< 0.1%
6.4 43
< 0.1%
6.333333333 53
< 0.1%

pa2_prom
Real number (ℝ)

Distinct1326
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.2114531
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.4 MiB
2023-07-30T19:09:00.591197image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2.8030303
Q13.62
median4.2115385
Q34.7777778
95-th percentile5.5454545
Maximum10
Range9
Interquartile range (IQR)1.1577778

Descriptive statistics

Standard deviation0.8832913
Coefficient of variation (CV)0.20973552
Kurtosis1.9439154
Mean4.2114531
Median Absolute Deviation (MAD)0.57640333
Skewness0.34042117
Sum670126.41
Variance0.78020351
MonotonicityNot monotonic
2023-07-30T19:09:00.729074image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4 1314
 
0.8%
4.428571429 1048
 
0.7%
3.5 750
 
0.5%
5 730
 
0.5%
4.5 641
 
0.4%
4.166666667 630
 
0.4%
3 559
 
0.4%
4.75 483
 
0.3%
4.875 405
 
0.3%
3.142857143 389
 
0.2%
Other values (1316) 152171
95.6%
ValueCountFrequency (%)
1 109
0.1%
1.236363636 100
0.1%
1.409090909 85
0.1%
1.542857143 121
0.1%
1.71875 105
0.1%
1.75 120
0.1%
1.769230769 33
 
< 0.1%
1.866666667 171
0.1%
1.894736842 98
0.1%
1.916666667 48
 
< 0.1%
ValueCountFrequency (%)
10 81
0.1%
9.285714286 8
 
< 0.1%
9 149
0.1%
8 135
0.1%
7.763157895 84
0.1%
7.078125 143
0.1%
6.909090909 92
0.1%
6.875 87
0.1%
6.787234043 52
 
< 0.1%
6.78 104
0.1%

final_prom
Real number (ℝ)

Distinct509
Distinct (%)0.6%
Missing76041
Missing (%)47.8%
Infinite0
Infinite (%)0.0%
Mean3.9574953
Minimum1.59375
Maximum8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.4 MiB
2023-07-30T19:09:00.873993image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1.59375
5-th percentile2.5
Q13.3333333
median4
Q34.5833333
95-th percentile5.3529412
Maximum8
Range6.40625
Interquartile range (IQR)1.25

Descriptive statistics

Standard deviation0.89474711
Coefficient of variation (CV)0.22608924
Kurtosis0.13885619
Mean3.9574953
Median Absolute Deviation (MAD)0.625
Skewness0.12662448
Sum328784.76
Variance0.80057239
MonotonicityNot monotonic
2023-07-30T19:09:01.011341image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4 2508
 
1.6%
3 1394
 
0.9%
5 979
 
0.6%
3.5 897
 
0.6%
4.333333333 863
 
0.5%
4.5 826
 
0.5%
3.333333333 709
 
0.4%
4.25 684
 
0.4%
2 676
 
0.4%
4.666666667 671
 
0.4%
Other values (499) 72872
45.8%
(Missing) 76041
47.8%
ValueCountFrequency (%)
1.59375 101
 
0.1%
1.666666667 47
 
< 0.1%
1.8 105
 
0.1%
1.928571429 127
 
0.1%
1.954545455 145
 
0.1%
2 676
0.4%
2.066666667 122
 
0.1%
2.090909091 117
 
0.1%
2.111111111 124
 
0.1%
2.125 91
 
0.1%
ValueCountFrequency (%)
8 14
 
< 0.1%
7.552631579 129
0.1%
7.5 29
 
< 0.1%
7 31
 
< 0.1%
6.666666667 15
 
< 0.1%
6.571428571 50
 
< 0.1%
6.55 103
0.1%
6.511627907 84
0.1%
6.5 59
< 0.1%
6.333333333 123
0.1%

edad
Real number (ℝ)

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.2633861
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.4 MiB
2023-07-30T19:09:01.134627image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q13
median3
Q33
95-th percentile5
Maximum10
Range9
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.74989546
Coefficient of variation (CV)0.2297906
Kurtosis13.562877
Mean3.2633861
Median Absolute Deviation (MAD)0
Skewness2.8771284
Sum519270
Variance0.5623432
MonotonicityNot monotonic
2023-07-30T19:09:01.235047image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
3 119734
75.2%
4 25520
 
16.0%
2 5353
 
3.4%
5 5018
 
3.2%
6 1726
 
1.1%
7 897
 
0.6%
8 435
 
0.3%
1 239
 
0.2%
9 142
 
0.1%
10 56
 
< 0.1%
ValueCountFrequency (%)
1 239
 
0.2%
2 5353
 
3.4%
3 119734
75.2%
4 25520
 
16.0%
5 5018
 
3.2%
6 1726
 
1.1%
7 897
 
0.6%
8 435
 
0.3%
9 142
 
0.1%
10 56
 
< 0.1%
ValueCountFrequency (%)
10 56
 
< 0.1%
9 142
 
0.1%
8 435
 
0.3%
7 897
 
0.6%
6 1726
 
1.1%
5 5018
 
3.2%
4 25520
 
16.0%
3 119734
75.2%
2 5353
 
3.4%
1 239
 
0.2%

prom_edad
Real number (ℝ)

Distinct1361
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.2633861
Minimum2
Maximum4.7714286
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.4 MiB
2023-07-30T19:09:01.370554image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile2.9067797
Q13.0942029
median3.2142857
Q33.3933333
95-th percentile3.7946429
Maximum4.7714286
Range2.7714286
Interquartile range (IQR)0.29913043

Descriptive statistics

Standard deviation0.27514447
Coefficient of variation (CV)0.084312569
Kurtosis1.5655518
Mean3.2633861
Median Absolute Deviation (MAD)0.13809524
Skewness0.73045161
Sum519270
Variance0.075704477
MonotonicityNot monotonic
2023-07-30T19:09:01.507932image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3 1409
 
0.9%
3.25 924
 
0.6%
3.166666667 582
 
0.4%
3.214285714 518
 
0.3%
3.5 500
 
0.3%
3.333333333 486
 
0.3%
3.181818182 484
 
0.3%
3.125 480
 
0.3%
3.2 475
 
0.3%
3.285714286 469
 
0.3%
Other values (1351) 152793
96.0%
ValueCountFrequency (%)
2 1
 
< 0.1%
2.403100775 129
0.1%
2.490196078 102
0.1%
2.5 70
< 0.1%
2.513513514 111
0.1%
2.524475524 143
0.1%
2.535211268 142
0.1%
2.538461538 78
< 0.1%
2.540229885 87
0.1%
2.553191489 94
0.1%
ValueCountFrequency (%)
4.771428571 35
 
< 0.1%
4.396825397 63
< 0.1%
4.392156863 51
< 0.1%
4.375 16
 
< 0.1%
4.354166667 48
< 0.1%
4.344827586 58
< 0.1%
4.340206186 97
0.1%
4.32 50
< 0.1%
4.27027027 37
 
< 0.1%
4.229885057 87
0.1%

condiciĂ³n
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
Abandona1
52133 
Examen
33615 
Insuficiente
30888 
Abandona2
25680 
Promociona
16804 

Length

Max length12
Median length10
Mean length9.0541918
Min length6

Characters and Unicode

Total characters1440703
Distinct characters21
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAbandona2
2nd rowAbandona1
3rd rowPromociona
4th rowExamen
5th rowAbandona1

Common Values

ValueCountFrequency (%)
Abandona1 52133
32.8%
Examen 33615
21.1%
Insuficiente 30888
19.4%
Abandona2 25680
16.1%
Promociona 16804
 
10.6%

Length

2023-07-30T19:09:01.633616image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-30T19:09:01.764635image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
abandona1 52133
32.8%
examen 33615
21.1%
insuficiente 30888
19.4%
abandona2 25680
16.1%
promociona 16804
 
10.6%

Most occurring characters

ValueCountFrequency (%)
n 267821
18.6%
a 206045
14.3%
o 128225
 
8.9%
e 95391
 
6.6%
i 78580
 
5.5%
b 77813
 
5.4%
A 77813
 
5.4%
d 77813
 
5.4%
1 52133
 
3.6%
m 50419
 
3.5%
Other values (11) 328650
22.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1203770
83.6%
Uppercase Letter 159120
 
11.0%
Decimal Number 77813
 
5.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 267821
22.2%
a 206045
17.1%
o 128225
10.7%
e 95391
 
7.9%
i 78580
 
6.5%
b 77813
 
6.5%
d 77813
 
6.5%
m 50419
 
4.2%
c 47692
 
4.0%
x 33615
 
2.8%
Other values (5) 140356
11.7%
Uppercase Letter
ValueCountFrequency (%)
A 77813
48.9%
E 33615
21.1%
I 30888
 
19.4%
P 16804
 
10.6%
Decimal Number
ValueCountFrequency (%)
1 52133
67.0%
2 25680
33.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1362890
94.6%
Common 77813
 
5.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 267821
19.7%
a 206045
15.1%
o 128225
9.4%
e 95391
 
7.0%
i 78580
 
5.8%
b 77813
 
5.7%
A 77813
 
5.7%
d 77813
 
5.7%
m 50419
 
3.7%
c 47692
 
3.5%
Other values (9) 255278
18.7%
Common
ValueCountFrequency (%)
1 52133
67.0%
2 25680
33.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1440703
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 267821
18.6%
a 206045
14.3%
o 128225
 
8.9%
e 95391
 
6.6%
i 78580
 
5.5%
b 77813
 
5.4%
A 77813
 
5.4%
d 77813
 
5.4%
1 52133
 
3.6%
m 50419
 
3.5%
Other values (11) 328650
22.8%

abandona1_p
Real number (ℝ)

Distinct1164
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.32763323
Minimum0
Maximum0.99145299
Zeros214
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size2.4 MiB
2023-07-30T19:09:01.903900image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.11538462
Q10.2231405
median0.31182796
Q30.41836735
95-th percentile0.59701493
Maximum0.99145299
Range0.99145299
Interquartile range (IQR)0.19522685

Descriptive statistics

Standard deviation0.14896633
Coefficient of variation (CV)0.45467404
Kurtosis1.3611815
Mean0.32763323
Median Absolute Deviation (MAD)0.096951924
Skewness0.76880349
Sum52133
Variance0.022190966
MonotonicityNot monotonic
2023-07-30T19:09:02.046085image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.3333333333 1890
 
1.2%
0.2 965
 
0.6%
0.5 792
 
0.5%
0.3636363636 737
 
0.5%
0.25 664
 
0.4%
0.4 625
 
0.4%
0.375 568
 
0.4%
0.2777777778 558
 
0.4%
0.1666666667 546
 
0.3%
0.3888888889 540
 
0.3%
Other values (1154) 151235
95.0%
ValueCountFrequency (%)
0 214
0.1%
0.01176470588 85
 
0.1%
0.01298701299 77
 
< 0.1%
0.01941747573 103
0.1%
0.02040816327 49
 
< 0.1%
0.025 40
 
< 0.1%
0.02564102564 78
 
< 0.1%
0.02597402597 77
 
< 0.1%
0.02666666667 75
 
< 0.1%
0.02739726027 73
 
< 0.1%
ValueCountFrequency (%)
0.9914529915 117
0.1%
0.9908256881 109
0.1%
0.9894736842 95
0.1%
0.987654321 81
0.1%
0.9868421053 76
< 0.1%
0.9866666667 75
< 0.1%
0.9848484848 66
< 0.1%
0.9830508475 59
< 0.1%
0.8867924528 53
< 0.1%
0.8260869565 46
 
< 0.1%

abandona2_p
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct764
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.24411052
Minimum0
Maximum0.98591549
Zeros10873
Zeros (%)6.8%
Negative0
Negative (%)0.0%
Memory size2.4 MiB
2023-07-30T19:09:02.191310image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.1744186
median0.24324324
Q30.31818182
95-th percentile0.43243243
Maximum0.98591549
Range0.98591549
Interquartile range (IQR)0.14376321

Descriptive statistics

Standard deviation0.11946407
Coefficient of variation (CV)0.48938516
Kurtosis1.3288989
Mean0.24411052
Median Absolute Deviation (MAD)0.07254623
Skewness0.15530078
Sum38842.866
Variance0.014271663
MonotonicityNot monotonic
2023-07-30T19:09:02.339149image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 10873
 
6.8%
0.25 2765
 
1.7%
0.3333333333 2423
 
1.5%
0.2 2076
 
1.3%
0.2307692308 1683
 
1.1%
0.2857142857 1615
 
1.0%
0.2727272727 1452
 
0.9%
0.1428571429 1140
 
0.7%
0.4 1052
 
0.7%
0.1666666667 1042
 
0.7%
Other values (754) 132999
83.6%
ValueCountFrequency (%)
0 10873
6.8%
0.01298701299 134
 
0.1%
0.01515151515 132
 
0.1%
0.02040816327 75
 
< 0.1%
0.02173913043 146
 
0.1%
0.02272727273 86
 
0.1%
0.02631578947 156
 
0.1%
0.02702702703 42
 
< 0.1%
0.02777777778 72
 
< 0.1%
0.0303030303 104
 
0.1%
ValueCountFrequency (%)
0.985915493 108
0.1%
0.9642857143 32
 
< 0.1%
0.7073170732 80
0.1%
0.6896551724 33
 
< 0.1%
0.6764705882 89
0.1%
0.65 129
0.1%
0.6428571429 58
< 0.1%
0.6 88
0.1%
0.5897435897 83
0.1%
0.5869565217 98
0.1%

Interactions

2023-07-30T19:08:50.433669image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-30T19:07:50.094509image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-30T19:07:54.663194image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-30T19:07:57.456523image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-30T19:08:00.081758image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-30T19:08:02.913246image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-30T19:08:05.459315image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-30T19:08:08.280835image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-30T19:08:10.779538image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-30T19:08:13.655551image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-30T19:08:16.453493image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-30T19:08:19.113126image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-30T19:08:22.049710image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-30T19:08:25.166551image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-30T19:08:28.019084image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-30T19:08:30.748467image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-30T19:08:33.597085image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-30T19:08:36.382938image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-30T19:08:39.037331image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-30T19:08:42.267001image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-30T19:08:44.944056image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-30T19:08:47.667284image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-30T19:08:50.578952image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-30T19:07:50.249228image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-30T19:07:54.823443image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-30T19:07:57.585307image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-30T19:08:00.212421image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-30T19:08:03.041503image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
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2023-07-30T19:08:16.071310image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-30T19:08:18.750254image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-30T19:08:21.659109image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-30T19:08:24.795289image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-30T19:08:27.620577image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-30T19:08:30.374144image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-30T19:08:33.205715image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-30T19:08:36.005663image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-30T19:08:38.682604image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-30T19:08:41.890087image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-30T19:08:44.576676image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-30T19:08:47.302069image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-30T19:08:50.068870image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-30T19:08:52.927867image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-30T19:07:54.359461image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-30T19:07:57.225951image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-30T19:07:59.853412image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-30T19:08:02.686449image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-30T19:08:05.214409image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-30T19:08:08.043235image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-30T19:08:10.538336image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-30T19:08:13.425702image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-30T19:08:16.200541image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-30T19:08:18.871642image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-30T19:08:21.791948image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-30T19:08:24.918968image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-30T19:08:27.754748image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-30T19:08:30.496224image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-30T19:08:33.336737image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-30T19:08:36.130052image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-30T19:08:38.802669image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-30T19:08:42.012910image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-30T19:08:44.702720image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-30T19:08:47.426562image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-30T19:08:50.191602image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-30T19:08:53.047626image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-30T19:07:54.506153image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-30T19:07:57.341901image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-30T19:07:59.971206image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-30T19:08:02.802227image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-30T19:08:05.320570image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-30T19:08:08.174019image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-30T19:08:10.662918image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-30T19:08:13.532410image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-30T19:08:16.330306image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-30T19:08:18.991333image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-30T19:08:21.918814image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-30T19:08:25.039453image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-30T19:08:27.895163image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-30T19:08:30.613912image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-30T19:08:33.470042image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-30T19:08:36.254287image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-30T19:08:38.921968image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-30T19:08:42.130081image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-30T19:08:44.823032image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-30T19:08:47.548452image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-07-30T19:08:50.312304image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Correlations

2023-07-30T19:09:02.487780image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
anioSEDEpa1pa2FinalcodCarrerarem1rem2estudiantecurson_alump_extrecursop_recursasalapa1_prompa2_promfinal_promedadprom_edadabandona1_pabandona2_pcuatMATERIAfacultadextranjeroturnocondiciĂ³n
anio1.0000.108-0.120-0.045-0.0570.226-0.179-0.1010.5510.9930.4790.1930.0790.1660.081-0.336-0.127-0.099-0.254-0.4800.4180.2840.0880.1660.3180.0730.0490.077
SEDE0.1081.0000.0010.054-0.0590.097-0.045-0.0310.0340.174-0.013-0.547-0.128-0.2490.9810.0300.219-0.142-0.108-0.260-0.136-0.0850.2200.3560.1410.1970.1940.066
pa1-0.1200.0011.0000.5760.172-0.0720.0860.040-0.079-0.114-0.040-0.0370.017-0.0320.0070.3210.153-0.026-0.0140.018-0.079-0.2120.0510.1450.0600.0690.0210.591
pa2-0.0450.0540.5761.0000.248-0.0100.1030.067-0.031-0.0360.008-0.049-0.018-0.0170.0540.1490.320-0.001-0.027-0.023-0.095-0.1040.0710.0520.0430.0570.0080.732
Final-0.057-0.0590.1720.2481.000-0.0180.1490.1020.020-0.0480.0020.015-0.123-0.112-0.0490.0070.0360.414-0.060-0.086-0.114-0.0810.1160.0330.0230.0000.0730.253
codCarrera0.2260.097-0.072-0.010-0.0181.0000.0060.0140.1580.2360.1460.065-0.067-0.0020.080-0.148-0.0160.001-0.032-0.1020.0870.0540.2190.9051.0000.1990.1080.119
rem1-0.179-0.0450.0860.1030.1490.0061.0000.138-0.088-0.172-0.048-0.023-0.129-0.103-0.0260.0210.0220.1000.0030.014-0.169-0.1120.0770.0000.0170.0000.0720.060
rem2-0.101-0.0310.0400.0670.1020.0140.1381.000-0.077-0.1160.011-0.017-0.0250.080-0.036-0.055-0.080-0.106-0.0410.024-0.0180.0600.1430.0910.0740.0410.0481.000
estudiante0.5510.034-0.079-0.0310.0200.158-0.088-0.0771.0000.5530.3060.211-0.086-0.0190.017-0.160-0.0550.031-0.530-0.3980.1330.0790.1010.1140.1590.9070.1350.099
curso0.9930.174-0.114-0.036-0.0480.236-0.172-0.1160.5531.0000.4760.1630.0640.1360.147-0.316-0.098-0.094-0.263-0.5060.3990.2600.1720.1910.2740.0710.0610.074
n_alum0.479-0.013-0.0400.0080.0020.146-0.0480.0110.3060.4761.0000.2090.0330.075-0.010-0.1070.027-0.009-0.162-0.3290.0950.0880.1240.1930.0930.0740.1260.028
p_ext0.193-0.547-0.037-0.0490.0150.065-0.023-0.0170.2110.1630.2091.0000.1160.232-0.546-0.122-0.1670.1200.0280.1350.2320.1510.1420.2130.1040.2950.1150.042
recurso0.079-0.1280.017-0.018-0.123-0.067-0.129-0.025-0.0860.0640.0330.1161.0000.488-0.126-0.056-0.027-0.1140.0790.1630.1060.1220.1110.0570.0590.0330.1170.034
p_recursa0.166-0.249-0.032-0.017-0.112-0.002-0.1030.080-0.0190.1360.0750.2320.4881.000-0.244-0.104-0.046-0.2000.0880.3080.2030.2310.2000.2090.0880.0600.3130.061
sala0.0810.9810.0070.054-0.0490.080-0.026-0.0360.0170.147-0.010-0.546-0.126-0.2441.0000.0460.218-0.129-0.099-0.242-0.145-0.0920.5090.6760.2250.2100.4780.113
pa1_prom-0.3360.0300.3210.1490.007-0.1480.021-0.055-0.160-0.316-0.107-0.122-0.056-0.1040.0461.0000.452-0.1280.0310.043-0.177-0.6310.1540.2990.1100.0420.0990.111
pa2_prom-0.1270.2190.1530.3200.036-0.0160.022-0.080-0.055-0.0980.027-0.167-0.027-0.0460.2180.4521.000-0.045-0.019-0.060-0.271-0.3020.1740.1100.0640.0490.0740.103
final_prom-0.099-0.142-0.026-0.0010.4140.0010.100-0.1060.031-0.094-0.0090.120-0.114-0.200-0.129-0.128-0.0451.000-0.062-0.160-0.202-0.0630.1790.1050.0680.0530.1800.038
edad-0.254-0.108-0.014-0.027-0.060-0.0320.003-0.041-0.530-0.263-0.1620.0280.0790.088-0.0990.031-0.019-0.0621.0000.4110.0620.0530.0850.0380.0630.0250.1300.087
prom_edad-0.480-0.2600.018-0.023-0.086-0.1020.0140.024-0.398-0.506-0.3290.1350.1630.308-0.2420.043-0.060-0.1600.4111.0000.2180.1650.2520.1400.0790.0450.3290.046
abandona1_p0.418-0.136-0.079-0.095-0.1140.087-0.169-0.0180.1330.3990.0950.2320.1060.203-0.145-0.177-0.271-0.2020.0620.2181.0000.3350.1740.1860.1000.0770.3150.166
abandona2_p0.284-0.085-0.212-0.104-0.0810.054-0.1120.0600.0790.2600.0880.1510.1220.231-0.092-0.631-0.302-0.0630.0530.1650.3351.0000.1710.0850.0540.0430.2030.128
cuat0.0880.2200.0510.0710.1160.2190.0770.1430.1010.1720.1240.1420.1110.2000.5090.1540.1740.1790.0850.2520.1740.1711.0000.1140.2110.0250.0440.058
MATERIA0.1660.3560.1450.0520.0330.9050.0000.0910.1140.1910.1930.2130.0570.2090.6760.2990.1100.1050.0380.1400.1860.0850.1141.0000.9050.0460.0770.070
facultad0.3180.1410.0600.0430.0231.0000.0170.0740.1590.2740.0930.1040.0590.0880.2250.1100.0640.0680.0630.0790.1000.0540.2110.9051.0000.1400.0900.084
extranjero0.0730.1970.0690.0570.0000.1990.0000.0410.9070.0710.0740.2950.0330.0600.2100.0420.0490.0530.0250.0450.0770.0430.0250.0460.1401.0000.0210.097
turno0.0490.1940.0210.0080.0730.1080.0720.0480.1350.0610.1260.1150.1170.3130.4780.0990.0740.1800.1300.3290.3150.2030.0440.0770.0900.0211.0000.093
condiciĂ³n0.0770.0660.5910.7320.2530.1190.0601.0000.0990.0740.0280.0420.0340.0610.1130.1110.1030.0380.0870.0460.1660.1280.0580.0700.0840.0970.0931.000

Missing values

2023-07-30T19:08:53.256435image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-07-30T19:08:53.844217image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-07-30T19:08:54.578489image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

aniocuatSEDEMATERIApa1pa2FinalcodCarrerafacultadrem1rem2estudianteextranjerocursoturnon_alump_extrecursop_recursasalapa1_prompa2_promfinal_promedadprom_edadcondiciĂ³nabandona1_pabandona2_p
020111132.0NaNNaN26INGENIERIANaNNaN2486000A710.07042300.0103.3409093.1428574.14285733.605634Abandona20.3802820.363636
12011113NaNNaNNaN11CS EXACTAS Y NATURALESNaNNaN2841800A710.07042300.0103.3409093.1428574.14285733.605634Abandona10.3802820.363636
220111138.07.0NaN29INGENIERIANaNNaN2616000A710.07042300.0103.3409093.1428574.14285733.605634Promociona0.3802820.363636
320111134.04.03.029INGENIERIANaNNaN2144500A710.07042300.0103.3409093.1428574.14285743.605634Examen0.3802820.363636
42011113NaNNaNNaN28INGENIERIANaNNaN5406300A710.07042300.0103.3409093.1428574.14285733.605634Abandona10.3802820.363636
52011113NaNNaNNaN39MEDICINANaNNaN3194900A710.07042300.0103.3409093.1428574.14285733.605634Abandona10.3802820.363636
620111135.04.05.011CS EXACTAS Y NATURALESNaNNaN3132500A710.07042300.0103.3409093.1428574.14285733.605634Examen0.3802820.363636
720111134.03.0NaN14CS EXACTAS Y NATURALESNaNNaN3012600A710.07042300.0103.3409093.1428574.14285733.605634Insuficiente0.3802820.363636
820111137.02.01.011CS EXACTAS Y NATURALESNaNNaN1608700A710.07042300.0103.3409093.1428574.14285743.605634Examen0.3802820.363636
92011113NaNNaNNaN11CS EXACTAS Y NATURALESNaNNaN3584500A710.07042300.0103.3409093.1428574.14285733.605634Abandona10.3802820.363636
aniocuatSEDEMATERIApa1pa2FinalcodCarrerafacultadrem1rem2estudianteextranjerocursoturnon_alump_extrecursop_recursasalapa1_prompa2_promfinal_promedadprom_edadcondiciĂ³nabandona1_pabandona2_p
2336012019221531.03.0NaN39MEDICINANaNNaN13056202634B730.04109610.2602741202.7343753.9512204.72222232.671233Insuficiente0.1232880.359375
233602201921531.02.0NaN47MEDICINANaNNaN14638712515C1510.30463600.42384142.6329113.620000NaN33.139073Insuficiente0.4701990.375000
233603201926539.08.0NaN30INGENIERIANaNNaN3525902590C1270.07086610.543307732.9785715.480000NaN43.149606Promociona0.4488190.285714
2336042019210534.06.03.04AGRONOMIA0.0NaN12421002600C1280.05468810.328125842.6619724.1730773.66666732.812500Examen0.4375000.277778
23360720192634.04.5NaN30INGENIERIANaNNaN13698902585A1430.02797200.020979753.9500004.475000NaN22.524476Examen0.3006990.200000
23360920192233.02.0NaN14CS EXACTAS Y NATURALESNaNNaN12625402529A1330.06015000.015038143.3707874.9830513.90000032.669173Insuficiente0.3308270.337079
2336102019210530.01.0NaN39MEDICINANaNNaN12309502599C1380.14492800.333333783.4936714.6865673.40000032.884058Insuficiente0.4275360.151899
233611201925533.0NaNNaN14CS EXACTAS Y NATURALESNaNNaN7159102572A1600.19375010.281250513.5458723.804598NaN32.875000Abandona20.3187500.201835
2336122019210532.0NaNNaN39MEDICINANaNNaN13556502593A1840.38043500.065217944.2735044.054348NaN22.760870Abandona20.3641300.213675
233614201922534.01.0NaN39MEDICINANaNNaN9440102541C1110.19819800.522523194.1492545.0204083.76470632.990991Insuficiente0.3963960.268657